Manufacturing design and process analysis system

ABSTRACT

Methods, apparatuses and systems that facilitate the design, production and/or measurement tasks associated with manufacturing and other processes. In one embodiment, the present invention provides an understanding of how the multiple characteristics of a given process output are related to each other and to process inputs. This knowledge facilitates a reduction in measurement costs. It also facilitates an understanding of the sometimes complex interrelationships between design targets, design tolerances, process inputs, process control variables, average process output and variation in the process output. As discussed in more detail below, embodiments of the present invention facilitate 1.) determination of design target values, 2.) determination of design specification limits, 3.) design of process inputs, 4.) determination of process control variable settings, and/or 5.) reduction of measurement costs.

CROSS-REFERENCE TO RELATED APPLICATION

The present application is a continuation application and commonly-ownedU.S. patent application Ser. No. 10/067,074 filed Feb. 4, 2002 now U.S.Pat. No. 7,072,808, entitled “Manufacturing Design and Process AnalysisSystem,” which is incorporated hereto by reference for all purposes.

FIELD OF THE INVENTION

The present invention relates to manufacturing, design and processengineering and, in one embodiment, to methods, apparatuses and systemsfacilitating the design, production and/or measurement tasks associatedwith manufacturing and other processes.

BACKGROUND OF THE INVENTION

The world of manufacturing, including process engineering, has beenunder continuous and accelerating pressure to improve quality and reducecosts. This trend shows signs of further accelerating rather thandecelerating. From a manufacturing perspective, quality refers toproducing parts that 1.) are close to or at engineering design targets,and 2.) exhibit minimal variation. The realm of design engineering hasalso been under continuous pressure to improve quality and reduce costs.Design engineering must create nominal design targets and establishtolerance limits where it is possible for manufacturing to produce partsthat are 1.) on target and 2.) that fall within the design tolerancelimits. In-other-words, engineers are tasked not only with designingarticles to meet form, fit and function, but with designing them forproducibility.

In any manufacturing or other process that depends on the laws ofengineering and physics to produce a useful result, there are fivefundamental elements (see FIG. 1): 1) the process that makes the product(A); 2) Inputs into the process (B); 3) Output from the process (C); 4)Process control variables adjusted to influence the process output (D);and, 5) uncontrolled process variables that influence the process (E)(e.g., either uncontrollable variables or variables that are leftuncontrolled because of time, cost or other considerations, collectivelyreferred to as “noise.”).

The traditional approach to producing articles, such as parts or othercomponents, that meet design specifications is a logical one based on asearch for causation. This approach is based on the principle that,control over the variables that influence a process yields control overthe output of that process. In-other-words, if one can control thecause, then one can also control the effect. FIG. 2 illustrates thisprior art principle, where an attempt is made to determine therelationships, linkages, or correlations between the control variablesand the characteristics of the output (e.g., manufactured parts).

Unfortunately, many manufacturing processes act like a black box. It canbe difficult in some of these cases to determine the relationshipbetween the process control variables and the resulting articlecharacteristic values. Furthermore, time and economic constraints canmake such a determination impractical even when this might betechnically possible.

Plastic injection molding is an example of this situation. With at least22 control variables, even when these control settings have only twolevels each (a high and a low temperature, a high and a pressure, etc.),there are nevertheless over 4 million possible combinations. Indeed,there are billions of possible combinations when three levels (high,medium and low settings) are considered. Furthermore, changes to processvariables may have varying effects on the resulting articlecharacteristics; for example, increasing a pressure setting can increasea first article characteristic, decrease a second, and not affect athird. Simple interactions, complex interactions and non-linearitiescomplicate the situation further. Further, there are usually multiplemold cavities in a single mold. Finally, there are numerous articlecharacteristics (dimensional, performance, or other requirements) thatmust be met. In light of the preceding, it is often extremely difficultto establish the combination of factors from the large number of partdesign targets, part tolerance limits, mold design characteristics andinjection molding press settings that produces acceptable articles.

Some progress has been made in this regard. Design of Experiments (DOE)methodology greatly reduces the number of experiments that must beconducted to understand the impact of a selected subset of controlvariables on the resulting output of a process. Unfortunately, evenafter performing a designed experiment, there are still a large numberof control variables that can affect the resulting articles. In anyevent, extensive measurement of produced parts is still conducted byboth the supplier and the OEM customer to ensure that acceptablearticles are produced.

In addition, there are two main paths to achieving improvedmanufacturing quality. The first is to measure the parts after they areproduced and then compare the parts to specification requirements(design targets and tolerances). This is an “on-line” process utilizingfeedback. The parts are usually measured, to some extent, by both theproducer and the customer (OEM, first tier manufacturer, second tiermanufacturer, etc.). Measuring the parts, recording and analyzing thedata, and reporting the results, however, is a very expensive andresource consuming process.

In their efforts to improve quality, many manufacturers have begun touse the techniques of Statistical Process Control (SPC) and ProcessCapability studies. Indeed, many customers require their suppliers toperform SPC or other equivalent measurement, recording, analysis andreporting procedures. According to this technique, samples are takenfrom the production line, measured and then analyzed to see if anyabnormal (not normally distributed) patterns or data points emerge. Ifsuch abnormal data points are detected, the process is considered“out-of-control” (i.e., failing to yield a consistent predictableoutput) and production is immediately stopped to fix the process. Themeasurement data from manufactured parts is analyzed using sophisticatedSPC statistical methods and charting tools embodied in specializedcomputer programs. Since most parts have many different dimensions,measurement and SPC analysis have usually been applied to a large numberof part dimensions for each part, increasing the time and expenseassociated with production. However, SPC is far less expensive in thelong run than shipping unacceptable parts and/or having to sortacceptable parts from unacceptable parts.

It has also been difficult for manufacturers (and their customers) todetermine 1.) what percentage of the dimensions should be monitoredusing SPC and 2.) which dimensions should be measured if the full set ofdimensions is not monitored. Usually, most, if not all, of the“critical” dimensions called out by the design engineer are measured andanalyzed using SPC techniques. However, economic constraints can resultin fewer than the desired number of dimensions being measured andanalyzed. Guesswork is then frequently involved as to which dimensionsto select for SPC or other analysis.

A second path to improving manufacturing quality is by reducing thenatural variation in the manufactured articles. The accuracy ofmaintaining the process control factors can be improved and/or the“noise” factors can be eliminated or minimized. This is an “off-line”process improvement using feedforward. Reducing natural variation isalso an expensive proposition since many relatively small common causesof variation are present. The degree to which the natural variation inthe parts produced must be reduced is usually determined throughexpensive process capability studies, usually conducted on each“critical” dimension.

In light of the foregoing, a need in the art exists for methods,apparatuses and systems facilitating design and manufacturing processesand, more particularly, addressing the problems discussed above. Forexample, a need in the art exists for methods and systems that allow forreductions in time and cost associated with the measurement, recording,analysis and reporting processes discussed above in connection with, forexample, SPC studies, Process Capability studies, shipping inspectionand receiving inspection. A need in the art exists for methods todetermine how to adjust inputs to a process in order to achieve thedesired outputs. A need in the art also exists for methods and systemsfacilitating a determination of how many article characteristics (e.g.,dimensions, performance measures, etc.) should be measured for a givenprocess. Lastly, a need in the art exists for methods and systems thatenable an assessment of which article characteristics should be measuredfor a given process. As discussed in more detail below, embodiments ofthe present invention substantially fulfill these needs.

SUMMARY OF THE INVENTION

The present invention provides methods, apparatuses and systems thatfacilitate the design, production and/or measurement tasks associatedwith manufacturing and other processes. In one embodiment, the presentinvention provides an understanding of how the multiple characteristicsof a given process output are related to each other and to processinputs. This knowledge facilitates a reduction in measurement costs. Italso facilitates an understanding of the sometimes complexinterrelationships between design targets, design tolerances, processinputs, process control variables, average process output and variationin the process output. As discussed in more detail below, embodiments ofthe present invention facilitate 1.) determination of design targetvalues, 2.) determination of design specification limits, 3.) design ofprocess inputs, 4.) determination of process control variable settings,and/or 5.) reduction of measurement costs.

The present invention uses analytical techniques to accomplish thepreceding objectives and advantages. As discussed below, graphicaltechniques, in one embodiment, can optionally be used in place ofanalytical techniques. Graphical techniques, including but not limitedto charts, graphs, and plots, can also be used to display analysisresults. The present invention employs powerful statisticalmethodologies that, in one embodiment, allow for a determination ofwhich and how many article characteristics should be measured,potentially reducing the cost and resource expenditure associated withmeasurement, recording, analysis and reporting. Embodiments of thepresent invention also assist design engineers in designing articles forproducibility. Embodiments of the present invention can also beconfigured to provide critical information necessary for designengineers and tooling engineers to modify design requirements forprocess inputs in order to make it possible for manufacturing to hitdesign targets and stay within specification tolerance limits.Embodiments of the present invention can also be employed to identify,using a systems engineering approach, which article characteristics havethe most restrictive targets and specification tolerance limits. Suchinformation, for instance, allows for an evaluation of whether or nottolerances should be increased and, if so, which tolerances and on whicharticle characteristic. The present invention can also be employed toreduce the cost of performing process capability studies by reducing, insome cases dramatically so, the number of process capability studiesthat must be conducted. These and other aspects of the present inventionwill be become apparent from the following description of preferredembodiments of the present invention.

DESCRIPTION OF THE DRAWINGS

FIG. 1 is a process flow diagram generally applicable to manufacturingand other processes.

FIG. 2 is a process flow diagram illustrating a concept associated withapplication of prior art process control techniques to manufacturingprocesses.

FIG. 3 is a process flow diagram illustrating a concept associated withthe present invention as applied to manufacturing processes.

FIG. 4 is a scatter diagram setting forth a regression model between twoarticle characteristics.

FIG. 5 is a scatter diagram modeling the effect a change in processcontrol settings has on process output.

FIG. 6 is a scatter diagram illustrating the effect of changing processinputs.

FIG. 7 is a scatter diagram illustrating the combined effect of changingprocess control settings and process inputs.

FIG. 8 is a scatter diagram including prediction intervals associatedwith a regression model.

FIG. 9 provides a graphical user interface facilitating the input ofarticle characteristic data used in connection with an embodiment of thepresent invention.

FIG. 10 is a diagram of a simple linear regression model between twoarticle characteristics.

FIG. 11 is a functional block diagram illustrating an embodiment of acomputer hardware system suitable for use in connection with the presentinvention.

FIG. 12 is a scatter diagram illustrating the concepts associated withan embodiment of the present invention.

FIG. 13 is a scatter diagram including a linear regression model,prediction intervals, a target intersection, and upper and lowerspecification limits.

FIG. 14 is a scatter diagram illustrating the determination of themaximum allowable range and target value for a predictor characteristic.

FIG. 15 illustrates a system architecture according to an embodiment ofthe present invention.

FIG. 16 is a flow chart providing a method according to an embodiment ofthe present invention.

FIG. 17 is a flow chart diagram illustrating a method associated withdisplaying a regression model and associated analysis elements to auser.

FIG. 18 is a flow chart setting forth a method allowing for selection ofa predictor characteristic.

FIG. 19 is a spreadsheet table including a set of article characteristicvalues as to a plurality of article characteristics, correlationcoefficients and a value indicating the predictive capability of eacharticle characteristic.

FIG. 20 is a flow chart diagram providing a method allowing forpopulation of a correlation coefficient table.

FIG. 21 is a flow chart diagram illustrating a method associated withuse of the present invention according to one embodiment.

FIG. 22 illustrates a constraint table according to an embodiment of thepresent invention.

DESCRIPTION OF PREFERRED EMBODIMENT(S) I. Background And OperatingPrinciples

A. Principles And Concepts

The present invention utilizes several graphical, statistical andmathematical techniques directed to analyzing the relationship betweenarticle characteristics to achieve a novel design and manufacturingprocess analysis system. Among these are scatter diagrams, correlationcoefficients, coefficients of determination, linear, non-linear andmulti-variate regression, prediction intervals, adjusted predictionintervals, prediction using regression, prediction using predictionintervals, DOE, averages and weighted averages. FIG. 3 is a processdiagram illustrating how an aspect of the present invention differs fromprior art techniques. In a wide variety of manufacturing processes, andinjection molding in particular, there is often a strong relationshipbetween article characteristics resulting from a given process. Thepresent invention assesses the statistical strength of theserelationships and, when they are sufficiently strong, capitalizes ontheir existence to facilitate a variety of design, production andmeasurement tasks associated with manufacturing processes.

In understanding the difference between the prior art exemplified inFIG. 2 and the present invention exemplified in FIG. 3, it should benoted that the primary focus of FIG. 3 is the relationship that existsbetween the part characteristics. In the field of injection molding, thedifferent outputs (#1, #2, etc.) in FIG. 3 would typically refer todifferent part dimensions. In the present invention, the different partcharacteristics are not limited to dimensions, but could be any partattribute. In addition, the different part characteristics may in factinclude the same dimension across different parts produced in a singlecycle of a multi-cavity mold.

FIG. 4 graphically illustrates the relationship between two articlecharacteristics as set forth on a scatter diagram. FIG. 4, in one form,illustrates the relationship between a predictor characteristic andpredicted characteristic. As discussed below, the data points used togenerate the regression model are typically generated using at least twodifferent methods.

The first method consists of generating parts with no changes made tothe process settings. This generally corresponds to a normal productionrun. All processes are subject to variation in control variables,environmental conditions, wear and tear, and many other factors. Theseinfluences on the process cause natural variation in the process output.The process output from this method is then measured. One problem withthis method is that the process of measurement is like any other processin that the measurement process has its own source of variations whichresult in measurement error. If the size of the natural variation in thepart characteristics is small relative to measurement error, then themeasurement error will overwhelm the natural variation. In thisinstance, it is unlikely that any statistically significant correlationscould be established between the part characteristics.

Injection molding processes typically have relatively small naturalvariations as compared to typical measurement error. Consequently, thefirst method of generating injection molded parts for evaluation ofrelationships can be unproductive. Consequently, the second method forgenerating parts is more applicable for injection molded parts. However,other processes may exhibit sufficient natural variation in order to usethe above-identified method.

According to a second method, variation in part characteristics isinduced. In the case of injection molding, variation is induced bydeliberately changing process control settings. In this manner, thevariation in part characteristic values becomes large relative tomeasurement error. Correlations, to the extent that they exist, thenbecome apparent.

As previously mentioned, DOE is a method that assists in reducing anunmanageably large number of experimental conditions down to amanageable few experimental conditions. Since variation must be inducedin the field of injection molding, there is utility in using DOEtechniques to design an efficient experiment. Use of this method hasfurther utility in that there are commercially available computerapplications that efficiently analyze the data and report the results ofthe analysis. Thus, one beneficial by product of using DOE, is thatuseful information may be extracted from an experimental run. Inparticular, it is usually possible to identify at least one processcontrol setting that can be used to significantly affect the partcharacteristics of resulting output. The information obtained from DOEhas utility as it can be used to adjust a process control setting toachieve a desired change in the joint operating position of the partcharacteristic values along the regression model, as explained below.

There is a second advantage to inducing variation in an experimental runthat is not connected with any efficiency measure associated with usingDOE. This second advantage lies in the fact that the present invention,in one embodiment, identifies the process control settings that have thegreatest impact or influence on the part characteristics. The presentinvention may also rely, in part, on the experience of the injectionmolding press operators and associated manufacturing and processpersonnel to select those “high impact” control settings. It should benoted that in injection molding, the usual paradigm is to minimizechanges to the press settings. In contrast, the present invention seeksto maximize their impact for purposes of inducing part variation forfurther analysis. In-other-words, for the purpose of inducing variation,the present invention seeks out the “worst” control settings. The“worst” control settings from a production perspective become the “best”control settings from the perspective of inducing variation.

As previously noted, there are a large number, typically 22 or more,process control settings in the field of injection molding. The presentinvention, in one embodiment, incorporates “scientific” or “decoupledmolding” principles to identify the high impact press controls. As withDOE, it is not necessary to use “scientific”/“decoupled molding”principles, but it potentially provides additional identificationadvantages. Thus, when several, typically 3–5, of the highest impactcontrol settings are changed in the experimental run, the greatestamount of variation will be introduced into the part characteristics.This variation will be of two types. The first will be a translation ofthe joint operating position along the regression line. The second mayinduce scatter of the data points about the regression line. It isimportant to create a robust data set to yield, in turn, a robustregression model for prediction.

Finally, the use of DOE techniques provides additional information.Specifically, the use of DOE techniques to induce part variation furtherallows for an understanding of how the process control variables thatwere changed affect part characteristics and potentially how thesecontrol variables interact with each other.

As previously discussed, it is difficult to establish the relationshipbetween injection molding control settings and part characteristics forseveral reasons including the large number of control variables, thepotentially large number of part characteristics, simple interactions,complex interactions, non-linearities and other effects. One of thegreat utilities of this invention is that even though there may be manyprocess control variables that influence a part characteristic, andthose changes may influence any one part characteristic in a verycomplex manner, changes in these variables have a predictable effect onthe relationship between the predictor characteristic and at least oneremaining article characteristic. Accordingly, as discussed in moredetail below, the systems and methods of the present invention allowdesign engineers and process operators to rely on the values of apredictor characteristic in order to determine whether one or morepredicted characteristics complies with design specifications. Inaddition, the systems and methods of the present invention allow designengineers and process operators to focus on the predicted characteristicin efforts to adjust process output to comply with designspecifications. These and other advantages will become apparent from thedescription provided below.

The regression model of FIG. 4 assumes a straight-line relationshipbetween the two variables with all data points being on the straightline; however, a perfectly linear model is seldom achieved becauseperfect correlation is rare in the real world. FIG. 10 illustrates thescatter of data points on a scatter diagram. Although the data pointsexhibit scatter, they also indicate a strong trend or relationship. Inother words, by knowing the value of one of the two variables, it ispossible to predict the other variable with a relatively high degree ofaccuracy. As applied to the present invention, knowledge of the value ofthe predictor characteristic can yield reasonably accurate knowledge ofthe value of the predicted article characteristic. In practice, scatteramong the data points is caused by a number of factors. These includevariations caused by common cause noise, common cause fluctuations incontrol variables, common cause variations in the process inputs andcommon cause variations in the measurement system used to measure thepart characteristics. FIG. 10 also illustrates two parameters that aretypically used to define the regression model. These are the slope ofthe regression line and the Y-intercept; however, other parameters canbe used. The embodiment shown in FIG. 10 also illustrates a linearregression model. The present invention, however, is not limited to theuse of a linear model. A non-linear regression model, such as amulti-variate model can also be used in connection with the presentinvention.

FIG. 8 illustrates the addition of upper and lower prediction intervalsto the regression model. The area bounded by the prediction intervalsrepresents the feasible area of output of the process, as to the x-axisand y-axis characteristic, when natural variation and measurement errorare included. In-other-words, all of the complexities of the process are“eliminated” since they show up as the bounded area of feasible output.The complexities that are “eliminated” include the aforementionedprocess control variable simple interactions, complex interactions,non-linearities, etc.

Analysis of process output in this manner provides a variety of usefulinformation facilitating design and manufacturing processes. Forexample, FIG. 4, as well as others, also contains a representation ofthe intersection between the design target for the predictorcharacteristic and the predicted characteristic. Location of the targetintersection provides a great amount of useful information to designengineers and process operators, as it illustrates that, for situationillustrated by FIG. 4, it is impossible to intersect the targetintersection no matter how the process control settings are changed.

For didactic purposes, the description of preferred embodimentsprimarily details application of an embodiment of the present inventionto injection molding processes. The present invention, however, hasapplication to a variety of manufacturing processes, such as plating,semiconductor manufacturing, machining, and any other process wherematerial is added, subtracted, or otherwise changed in form orstructure. In addition, the present invention can be applied to aid thedesign of a manufactured article, the development of a process tomanufacture the article, and/or the reduction of measurement costs.Moreover, the present application has application to a variety ofarticles, including stand-alone articles or items, as well as articlesintended as components, elements or parts of a combination. Accordingly,the description of the preferred embodiments set forth herein refers to“articles” and “parts” interchangeably.

The present invention also has application in assessing the impact ofsources of variation other than variation caused by changes in presscontrol settings. Virtually any source of variation, if it causessufficient variation in the part characteristic value, can be assessed.Selected examples could include determining the effect of setup-to-setupvariation, determining the effect of press-to-press variation forinjection molding, determining temporal effects such as the impact ofseasonal effects and assessing the impact of different types of rawmaterial or the impacting of purchasing either raw material orcomponents from different suppliers.

In addition, embodiments of the present invention can be performedwithout the aid of a computing device, such as a personal computer, toperform various mathematical and statistical computations set forthherein. For a small number of article characteristics, it is entirelyfeasible to do all of the analysis and/or graphing by hand and/or with aspreadsheet. In a preferred embodiment, however, given the large amountsof data and computational requirements, various operations associatedwith the present invention are performed with a computing deviceconfigured to execute the operations described herein and displayresulting data on a user interface display.

B. Exemplary System Architecture

FIG. 15 is a simplified block diagram illustrating a system architectureaccording to one embodiment of the present invention. As FIG. 2provides, a system architecture includes process analysis application100 and operating system 130. Process analysis system 100 includes datainput module 102, regression module 104, correlation module 106, displaymodule 108, and interface application module 110. Data input module 102is operative to receive article characteristic data, as well as formatand store the data in a suitable format for operation by other modulesassociated with process analysis application 100. Regression module 104is operative to compute a regression model given a set of inputs.Correlation module 106 is operative to perform operations relating tothe correlations among article characteristics as more fully describedbelow. Display module 108, in one embodiment, is operative to generategraphical displays of regression and/or correlation relationships for agiven set of data, as well as other data elements as more fullydescribed below. Interface application module 110 is operative tocoordinate operation of the other modules associated with processanalysis system 100 based on commands received from a user.

In one embodiment, the above-described system architecture operates inconnection with computer hardware system 800 of FIG. 11. Operatingsystem 130 manages and controls the operation of system 800, includingthe input and output of data to and from process analysis application100, as well as other software applications (not shown). Operatingsystem 130 provides an interface, such as a graphical user interface(GUI), between the user and the software applications being executed onthe system. According to one embodiment of the present invention,operating system 130 is the Windows® 95/98/NT/XP operating system,available from Microsoft Corporation of Redmond, Wash. However, thepresent invention may be used with other conventional operating systems,such as the Apple Macintosh Operating System, available from AppleComputer Inc. of Cupertino, Calif., UNIX operating systems, LINUXoperating systems, and the like.

FIG. 11 illustrates one embodiment of a computer hardware systemsuitable for use with the present invention. In the illustratedembodiment, hardware system 800 includes processor 802 and cache memory804 coupled to each other as shown. Additionally, hardware system 800includes high performance input/output (I/O) bus 806 and standard I/Obus 808. Host bridge 810 couples processor 802 to high performance I/Obus 806, whereas I/O bus bridge 812 couples the two buses 806 and 808 toeach other. Coupled to bus 806 are network/communication interface 824,system memory 814, and video memory 816. In turn, display device 818 iscoupled to video memory 816. Coupled to bus 808 are mass storage 820,keyboard and pointing device 822, and I/O ports 826. Collectively, theseelements are intended to represent a broad category of computer hardwaresystems, including but not limited to general purpose computer systemsbased on the Pentium® processor manufactured by Intel Corporation ofSanta Clara, Calif., as well as any other suitable processor.

The elements of computer hardware system 800 perform their conventionalfunctions known in the art. In particular, network/communicationinterface 824 is used to provide communication between system 800 andany of a wide range of conventional networks, such as an Ethernet, tokenring, the Internet, etc. Mass storage 820 is used to provide permanentstorage for the data and programming instructions to perform the abovedescribed functions implemented in the system controller, whereas systemmemory 814 is used to provide temporary storage for the data andprogramming instructions when executed by processor 802. I/O ports 826are one or more serial and/or parallel communication ports used toprovide communication between additional peripheral devices which may becoupled to hardware system 800.

Hardware system 800 may include a variety of system architectures andvarious components of hardware system 800 may be rearranged. Forexample, cache 804 may be on-chip with processor 802. Alternatively,cache 804 and processor 802 may be packed together as a “processormodule”, with processor 802 being referred to as the “processor core”.Furthermore, certain implementations of the present invention may notrequire nor include all of the above components. For example, theperipheral devices shown coupled to standard I/O bus 808 may be coupledto high performance I/O bus 806; in addition, in some implementationsonly a single bus may exist with the components of hardware system 800being coupled to the single bus. Furthermore, additional components maybe included in system 800, such as additional processors, storagedevices, or memories.

In one embodiment, the elements of the present invention are implementedas a series of software routines run by hardware system 800 of FIG. 11.These software routines comprise a plurality or series of instructionsto be executed by a processor in a hardware system, such as processor802. Initially, the series of instructions are stored on a storagedevice, such as mass storage 820. However, the series of instructionscan be stored on any conventional storage medium, such as a diskette,CD-ROM, ROM, etc. Furthermore, the series of instructions need not bestored locally, and could be received from a remote storage device, suchas a server on a network, via network/communication interface 824. Theinstructions are copied from the storage device, such as mass storage820, into memory 814 and then accessed and executed by processor 802. Inone implementation, these software routines are written in the C++programming language and stored in compiled form on mass storage device820. However, these routines may be implemented in any of a wide varietyof programming languages, including Visual Basic, Java, etc. Inalternate embodiments, the present invention is implemented in discretehardware or firmware. For example, an application specific integratedcircuit (ASIC) could be programmed with the above described functions ofthe present invention.

II. Operation of Exemplary Embodiments

A. Generating A Set of Articles Having A Range of Variation As To APlurality of Article Characteristics

As described above, the present invention assesses the relationshipbetween article characteristics associated with a set of articles havinga range of variation as to the article characteristics. According to oneembodiment of the present invention, a user generates a set of partshaving a range of variation as to a plurality of article characteristicsaccording to a given process. For example, a user may install aninjection molding tool in an injection molding machine and produce a setof articles. The set of articles, or a sample thereof, are then measuredor otherwise inspected or assessed as to the article characteristics ofinterest. The resulting set of data is then recorded (e.g., such as inan Excel spread sheet table) and used for subsequent analysis.

A variety of article characteristics can be measured and analyzed. Forexample, measured or otherwise determined article characteristics caninclude the dimensions of the article (e.g., length, height, width,circumference, overall diameter, etc. of the article or a given featureof the article), hardness, porosity, bowing, smoothness, voidcharacteristics (whether voids exists and their number), color,strength, weight, and any other article characteristic, includingperformance characteristics such as the spray pattern of a nozzle orflow rate through a hydraulic restrictor.

As discussed above, the present invention can be applied to a set ofarticles where variation of the article characteristics occurs naturallyor is induced by varying process control variables associated with theprocess that creates the article. When articles are produced withunchanged process control settings, there is typically little naturalvariation in the resulting article characteristics. This is particularlytrue for injection molded plastic parts. Measurement error may obscureor otherwise render unreliable the natural variation observed from agiven set of articles. If it is not cost effective to use more precisemeasuring instruments, then variation should be induced in the parts byvarying process settings. Accordingly, in a preferred embodiment,article variation is induced when measurement error is large compared tonatural part variation.

A.1. Inducing Variation

Article variation can be induced by selecting and varying press settingsbased on the experience of the operator. That is, the operator can usehis experience to determine which process settings to change in order toinduce variation in the parts. To induce variation in a preferred form,the operator varies the process settings during the manufacturingprocess and allows the process to come to equilibrium between settingchanges before selecting parts for measurement. In addition, theoperator in a preferred embodiment of the method selects the set orsubset of process settings that induce the greatest variability in thearticle characteristics of interest. In a preferred embodiment, theupper and lower limits for the process settings are chosen such that theprocess produces parts without harming the process equipment or tooling.Moreover, in a preferred form, the magnitude of the changes in processsettings is chosen to induce variation across the full range between thearticle characteristic upper and lower specification limits for each ofthe article characteristics of interest.

As to injection molding processes, part variation, in one embodiment,may also be induced by selecting and varying process control settingsusing scientific/decoupled molding techniques. Scientific/decoupledmolding techniques provide a method of reducing the large number ofpress settings down to three or four key variables. Furthermore,scientific/decoupled molding techniques can be used in conjunction withthe experience of the mold press operator to determine which presssettings should be varied. In a preferred embodiment, the set ofarticles produced comprises articles from an adequate number ofrepetitions at each set of process control variables.

In a preferred embodiment, Design of Experiments (DOE) methodology isemployed to generate a set of articles having a range of variation. DOEcan be used irrespective of whether the determination of which processsettings to vary is made using operator experience, decoupled/scientificmolding principles, or some combination thereof. DOE defines efficientexperimental setups that allow the extraction of the maximum amount ofinformation for a relatively small experimental effort. Once it has beendetermined which press settings to change, DOE defines efficientexperimental setups that allow the extraction of the maximum amount ofinformation for a relatively small experimental effort. This applies toboth the design of the experiment (e.g., combinations of processsettings, and the number of replications at each combination, etc.) andto the analysis of the data. A wide variety of known DOE techniques andavailable software tools can be used to design the experimental run thatinduces part variation.

As discussed in more detail below, the use of DOE to produce a set ofarticles for analysis provides “bonus” information that can be used,after analysis according to the present invention, to move a givenarticle output closer to target and to reduce variation in the articles.For example, such information allows the operator to adjust presssettings to accomplish the following during production: 1) move productoutput to target, and/or 2) minimize product variation, and/or 3)minimize cost, and/or 4) minimize press cycle time.

A.2. Receiving Article Characteristic Values

In one embodiment, the present invention is implemented by a computingdevice (such as a special-purpose or general purpose computer)configured to execute the functionality described herein. After a givenset of articles is produced and article characteristics are measured, ina preferred form, a suitably configured computing device, executing datainput module 102, receives the article characteristic values associatedwith the set of articles and stores them in memory.

FIG. 9 sets forth a graphical user interface provided by an embodimentof the present invention that allows a user to input a set of articlecharacteristic values. As FIG. 9 illustrates, an embodiment of thepresent invention allows the user to open a data input database andmanually provide the set of article characteristics into a table. Oneembodiment, however, allows the user to import article characteristicvalue data stored in various file formats, such as in an Excel® spreadsheet table, or any other suitable file format. In one form, data inputmodule 102 is further operative to validate the data set, such aschecking for blank cells and other validation methods.

In addition, as discussed below, data input module 102 is operative toreceive other data associated with operation of embodiments of theinvention. For example, data input module 102 is operative to receivetarget values, as well as upper and lower specification limits, for allor a subset of article characteristics. In one embodiment, such data isused to provide users the ability to assess the relationship betweenprocess output and design specifications for a given set of processinputs.

B. Assessing Relationship Between Article Characteristics

To allow for an assessment of the relationship between articlecharacteristics associated with a set of articles, process analysisapplication 100, in one implementation, generates a set of scatterdiagrams each based on a pair of article characteristics. See FIG. 10.The set of scatter diagrams can represent all possible combinations ofarticle characteristics, or it can consist of a subset of all possiblecombinations.

In one embodiment, display module 108 generates graphical displaysincluding scatter diagrams for presentation on display device 818 toallow the user to visually assess the degree of correlation betweenarticle characteristics. See FIG. 10. In one form, the graphical userinterface presented on display device 818 allows the user to select,using keyboard and pointing device 822, a first article characteristicfor the x-axis and successively view the scatter diagrams based on thefirst article characteristic and the remaining article characteristicson the y-axis. The user can use the information gleaned from this visualinspection to assess the capability of the first characteristic to be anadequate predictor of the remaining article characteristics (see below).

B.1. Determining Regression Models Between Article Characteristics

Process analysis system 100 also includes regression module 104operative to determine the regression model between selected articlecharacteristics. As discussed above, display module 108 is operative togenerate a graphical display of regression models and present them ondisplay device 818. See FIG. 10. As FIG. 10 shows, the regression modelmay be plotted and displayed with (or optionally without) the underlyingdata points. In a preferred embodiment, regression module 104 computesregression models using “least squares” curve fitting methods. However,other methods can also be used. Although the various figures show alinear regression model, the regression model can be a linear, anon-linear (higher order polynomial) model or multi-variate.

The display of the relationship between two article characteristics inthis manner provides useful information to process operators, designengineers and others associated with the design and manufacture of thearticle. The slope (steepness) of the regression line can be used todetermine the relative sensitivity of article characteristics to changesin process settings. In addition, the slope (steepness) of theregression line can be used to identify article characteristics thatwill be more restrictive on (more sensitive to) the allowable range ofprocess settings when specification limits are considered (see below).

B.1.a. Locating the Target Intersection

As discussed above, the design of an article generally results in atarget value, as well as upper and lower specification limits, for eacharticle characteristic (or at least the critical articlecharacteristics). In one form, process analysis system 100 is operativeto determine the intersection of the target values for a pair of articlecharacteristics relative to the corresponding regression model. FIG. 4illustrates an exemplary regression model display including the targetintersection located relative to the regression model associated with afirst (predictor, see below) characteristic and a second articlecharacteristic.

As FIG. 4 illustrates, location of a target intersection allows for avisual and/or analytical determination of the direction and magnitudethat the regression line is offset from the target intersection for eacharticle characteristic. Moreover, as the regression model essentiallyrepresents all possible combination of process settings (that is,without changing a process input, such as changing the dimensions of amold cavity), the resulting diagram allows one to determine whetherproducing a part having a given pair of article characteristics attarget value is achievable.

In addition, as FIGS. 5 and 6 illustrate, the information provided byFIG. 4 facilitates the process of changing an aspect of the process(e.g., process inputs or control settings) to shift output closer todesign target. For example, as FIG. 5 illustrates, the operator canchange the combination of process control settings to shift the jointoperating position closer to a desired point along the regression model.In one embodiment of the present invention, the process control settingscan be changed to optimize the joint operating positions of more thantwo part characteristics. In addition, by changing process inputs, theregression line can be shifted to a position closer to the targetintersection or shifted to a position such that the regression linepasses through the target intersection. See FIG. 6. In one embodiment ofthe present invention, the process inputs can be changed to optimize theposition of more than one regression line. Lastly, as FIG. 7 provides,changes in both process control settings and process inputs can be usedto shift the part characteristic values closer to the targetintersection. In one embodiment of the present invention, changes inboth process control settings and process inputs can be changed tooptimize more than two part characteristic values.

In one embodiment, an offset table is created based on how far theregression line is located from (offset from) the target intersectionfor each article characteristic. The offset is presented in threeformats: in the X-direction, the Y-direction and the directionperpendicular to the regression line.

B.1.b. Specification Limits

Process analysis system 100 is also configured to locate the upper andlower specification limits for the Y-axis article characteristicrelative to the regression model between the Y-axis articlecharacteristic and an X-axis article characteristic. See FIG. 12. Thisgraphical representation allows the ability to determine whether any ofthe Y-axis article characteristics are robust against changes to processvariables. In such cases, the regression line will generally have asmall slope and/or not intersect either the upper or lower Y-axisspecification limits.

In addition, process analysis system 100 is also operative to locate theupper and lower specification limits for the X-axis articlecharacteristic relative to the regression model. This representationallows one to determine whether the regression line passes through theacceptable region bounded by the four specification limits. In otherwords, this representation allows for a determination as to whether itis even possible, given the current process and process inputs, tomanufacture the parts within specification limits. In addition, locatingthe specification limits relative to the regression model allows for adetermination of the maximum and minimum values (and, therefore, range)for the X-axis characteristic that will yield articles where the Y-axischaracteristic is within specification limits. This range determinationallows a manufacturer, for example, to determine if the part is incompliance with the specification limits for both the X- and Y-axischaracteristics only by measuring the X-axis characteristic. To computethe minimum X-axis article characteristic, process analysis system 100computes the value of the X-axis article characteristic at which theregression model intersects the lower specification limit for the Y-axischaracteristic. Similarly, to compute the maximum X-axis articlecharacteristic, process analysis system 100 computes the value of theX-axis characteristic at which the regression model intersects the upperspecification limit for the Y-axis characteristic. In either case, theX-axis characteristic can be no larger than the upper specificationlimit for X and can also be no smaller than the lower specificationlimit for X.

B.1.c. Prediction Intervals

As FIG. 8 shows, process analysis system 100 may also add upper andlower prediction intervals to the regression model diagram to allow fora determination of the magnitude of the variability about the regressionmodel. In one embodiment, regression module 104 is further operative tocompute upper and lower prediction intervals based on a set of articlecharacteristic value pairs using known statistical methods. As FIG. 8illustrates, locating the prediction intervals also allows for anevaluation of the variability relative to the target intersection. Forexample, the target intersection may lie outside of the predictionintervals on either the high or low side. In this case, it is virtuallyimpossible to ever hit the target intersection given the same processinputs. For example, assuming that FIG. 8 models the relationshipbetween two article characteristics resulting from an injection moldingprocess, locating the target intersection reveals that use of the mold,in its current state, will not obtain a part on target as to the twoarticle characteristics. Further, when the target intersection lieswithin the prediction intervals, the percentage of parts where thearticle characteristic is greater than target and less than target canbe determined through the use of known statistical techniques.

In addition, prediction intervals may also be used in the determinationof minimum and maximum values for the X-axis characteristic (see SectionII.B.1.b., supra). As FIG. 13 illustrates, to compute the minimum X-axisarticle characteristic, process analysis system 100 computes the valueof the X-axis article characteristic at which the lower predictioninterval intersects the specification limit for the Y-axischaracteristic. Similarly, to compute the maximum X-axis articlecharacteristic, process analysis system 100 computes the value of theX-axis characteristic at which the upper prediction interval intersectsthe upper specification limit for the Y-axis characteristic. In eithercase, the X-axis characteristic can be no smaller than its lowerspecification limit and no larger than its upper specification limit.

An embodiment of the present invention allows the user to determine themagnitude of the prediction intervals by inputting the percentage ofarea in the distribution that the user wants to have included in betweenthe prediction intervals.

B.2. Predictor Characteristic

An embodiment of the present invention applies correlation andregression analysis to determine predictor characteristics inmanufacturing processes. In one embodiment, a predictor characteristicis selected from the plurality of article characteristics associatedwith a part and used as the single X-axis characteristic. As discussedin more detail below, the predictor characteristic is chosen based on anassessment of the capability of a given article characteristic to be apredictor of other article characteristics. The selection of a predictorcharacteristic, therefore, reduces the number of article characteristiccombinations that must be analyzed to a relatively small subset. Forexample, a part having 31 article characteristics would require analysisof over 900 relationships between article characteristics. The selectionof a predictor characteristic reduces this to 30 combinations. Inaddition, the selection of a predictor characteristic can be used in avariety of ways to facilitate design, production, and measurement tasksassociated with manufacturing. For example, a predictor characteristiccan be used to greatly reduce the time and expense associated withmeasuring parts, as only the predictor characteristic needs to bemeasured during production to determine if all other articlecharacteristics are within specification.

FIG. 16 illustrates a method involving selection of a predictorcharacteristic according to an embodiment of the present invention. Asdiscussed above, data input module 102 is operative to receive and storearticle characteristic data associated with a set of articles (e.g.,article characteristic values and design targets/specification limits)(step 202). In one embodiment, correlation module 106, as discussed inmore detail below, is operative to perform calculations (e.g., such asthe determination of correlation coefficients between all combinationsof article characteristics, computation of overall predictive capabilityof each article characteristic, etc.) to rank article characteristicsaccording to their relative predictive capabilities. In one embodiment,display module 108 displays the ranked list of article characteristicsand allows for selection of an article characteristic as the predictorcharacteristic (see step 204). As discussed below, a user may choose apredictor characteristic based on a number of considerations includingrelative predictive capability, feasibility/cost of measuring thearticle characteristic, etc. Still further, the selection of a predictorcharacteristic may be based on other methods (see below).

With a selected predictor characteristic, interface application module110 directs regression module 104 to determine the regression modelbetween the predictor characteristic (in one embodiment, as the x-axischaracteristic) and all or a subset of the remaining articlecharacteristics (see steps 206, 208, 210 and 211). When complete, theuser is prompted to select a predicted article characteristic (step212). Display module 108, in one embodiment, based on the equationdefining the regression model, generates a graphical display of theregression model between the predictor characteristic and the selectedpredicted characteristic (step 214).

In addition to the regression model, display module 108 is furtheroperative to add additional features to the graphical representationpresented to users. FIG. 17 illustrates a method for generating agraphical representation illustrating the relationship between apredictor characteristic and a predicted characteristic with additionalfeatures discussed above. Display module 108 retrieves the regressionmodel between the selected predicted characteristic and the predictorcharacteristic (step 302). As FIG. 17 provides, display module 108 mayalso locate the intersection of the target values associated with thepredictor and predicted characteristics relative to the regression model(step 304) (see Section II.B.1.a., supra). Display module 108 may alsolocate the predictions intervals associated with the regression model onthe display (step 306) (see Section II.B.1.c., supra). Still further,display module 108 may locate the upper and lower specification limitsassociated with the predicted characteristic (step 308), as well as theupper and lower specification limits associated with the predictorcharacteristic (step 310). See Section II.B.1.b., supra. Display module108 may also graphically illustrate the minimum and maximum values forthe predicted characteristic based on the specification limits and,optionally, prediction intervals (step 312). See Sections II.B.1.b. &II.B.1.c., supra.

A variety of interface displays are possible. For example, the equationdefining the regression model may be displayed to the user. Moreover,the maximum and minimum predictor characteristic values may be displayedto the user, as well as any other data associated with the articlecharacteristics and/or the relationship between them. In one embodiment,the graphical user interface presented on display device 818 allows theuser to select which of the above graphical elements to display.

B.2.a. Selecting Predictor Characteristic

The predictor characteristic can be selected using either a heuristic ora statistically-based approach. Moreover, the selection of a predictorcharacteristic may be based on a visual assessment of the correlationsbetween article characteristics or an analytically-based assessment.

B.2.a.1. Graphical Selection

In one embodiment, a user can use the scatter diagrams to visuallyassess the degree of correlation, amounting to a visual estimation ofthe correlation coefficient for each scatter diagram. The closer theboundary or perimeter around the data points approaches a straight line,the higher the correlation coefficient. The exception to this generalrule is for situations where the regression line is horizontal, ornearly so. See Section II.B., supra. The user can assess the scatterdiagrams of all possible combinations of article characteristics.However, in another embodiment, the number of scatter diagrams usedcould be greatly reduced by picking one article characteristic to act asthe foundational variable. Using the foundational variable as the X-axisvariable, a scatter diagram would be then created for each remainingarticle characteristic, which would be plotted on the Y-axis. Pickingthe “foundational” (equivalent to the predictor) article characteristiccan be based on looking at the “scatter” of the data, or can be randomlypicked. While a visual assessment may be practical if a small number ofarticle characteristics are involved, larger numbers of articlecharacteristics, resulting in combinations into the thousands, requires(at least for practical purposes) the use of a computing device toanalytically select the predictor characteristic.

B.2.a.2. Analytical Selection of Predictor Characteristic

To facilitate selection of a predictor characteristic, in oneembodiment, correlation module 108 calculates the correlationcoefficients between all or a subset of the article characteristics;determines, based on the calculated correlation coefficients, a valueindicating the predictive capability of a first article characteristicrelative to all other article characteristics; and repeats this processfor all or a subset of the article characteristics. FIG. 18 provides amethod illustrating a process flow associated with selection of apredictor characteristic. As FIG. 18 shows, correlation module 106, asmore fully described below, calculates the correlation coefficientsbetween all or a selected subset of the article characteristics (basedon a set of article characteristic values, see FIG. 19, Section A) andpopulates a correlation coefficient table (FIG. 19, Section B) (step402). Correlation module 106 then computes a value indicating therelative predictive capability of each article characteristic (step404). In one embodiment, this value is the average of the absolutevalues of the correlation coefficients for a given articlecharacteristic (see FIG. 19, Section C). Of course, other methods forcomputing this value can be used, such as computing the average withoutabsolute values, computing a weighted average, etc.

Correlation module 106 then ranks the article characteristics accordingto the values computed in step 404 (step 406). Display module 108 thendisplays the ranked list on display device 818 to allow the user toselect a predictor characteristic based at least in part on thepredictive capabilities of the article characteristics (step 408).According to one embodiment, the user makes his choice (step 410),causing interface application module 110 to direct regression module 104to compute the regression models between the selected predictorcharacteristic and the remaining article (predicted) characteristics(see above).

The correlation coefficient table may be populated using any suitableprocess or technique. However, in a preferred embodiment, correlationmodule 106 executes the methodology described below.

B.2.a.3. Population of Correlation Coefficient Table

According to standard industry practice, the data for a single articlecharacteristic (e.g., one dimension) is vertically arranged in a column.Thus, each column stores the data for one and only one articlecharacteristic. The resulting measurement data array will, therefore,have as many columns as there are article characteristics. More oftenthan not, the data is stored in an Excel spreadsheet, or other suitablefile format.

With this convention, then, each column represents a different articlecharacteristic. Each row represents the article characteristic data(multiple article characteristics) for a single part. In the case ofinjection molding, each row stores the data associated with a singlepress operating cycle. If the mold is a single-cavity mold, each rowwill contain measurement data for a single part. However, if the mold isa 4-cavity mold, each row stores the measurement data for all four partsproduced during one machine cycle. Typically, the same articlecharacteristics are measured for each part in a multi-cavity mold;however, there is no constraint that requires this.

In one embodiment, correlation module 106 includes functionality todetermine the correlation coefficients (according to standardstatistical methods) among all article characteristics, compute a valueindicative of the predictive capability of each article characteristic,and rank the article characteristics according to their relativepredictive capabilities.

FIG. 20 illustrates a method for populating the correlation coefficienttable discussed above. As FIG. 20 shows, in one embodiment, correlationmodule 106 initializes a correlation coefficient table (step 502) andvariables associated with the cell parameters of the table (A, B) (step504) and the article characteristics (see steps 506 and 508). Fordidactic purposes, assume that correlation module 106 operates on thearticle characteristic values of FIG. 19, Section A. In one embodiment,correlation module 106 computes the correlation coefficient between thefirst article characteristic (X=1) and the second article characteristic(Y=2) based on the article characteristic values in the correspondingcolumns (step 510). Correlation module 106 then stores the computedcorrelation coefficient (in the example, 0.999232) in the upper lefthand corner of the table (A=1, B=1) (step 512). Correlation module 106then calculates the correlation coefficients between the first articlecoefficient (X=1) and the remaining coefficients (Y), increasing the rowposition (B) with each successive computation and store (see steps 514,516 and 518).

After correlation module 106 reaches the last remaining articlecharacteristic (step 514), it fetches the computed correlationcoefficients in the first column (A=1), transposes the column into arow, shifts the row by one relative to the correlation coefficient tableand stores the data in the appropriate cells of the table (step 518).Correlation module 106 then increments the cell column position (A=2)and the article characteristic identifiers (X=2) (step 522), (Y=3) (step508) and sets the cell row position equal to the column position (B=2)(step 524). Correlation module 106 then computes the correlationcoefficient between the second article characteristic (X=2) and thethird article characteristic (Y=3; step 508) (step 510) and stores it inthe appropriate cell (A=2, B=2) (step 512). Correlation module 106repeats this process until the correlation coefficient between thesecond-to-last article characteristic value and the last articlecharacteristic value has been computed and stored (see step 520). AsFIG. 19 illustrates, the resulting columns of correlation coefficients,each column corresponding to an article characteristic, allows forrelative easy computation of a value (e.g., an average) indicating thepredictive capabilities of the article characteristics (see FIG. 19,section C).

As is apparent from the above-provided description, the process forpopulating the correlation coefficient table achieves a 50% reduction inthe number of correlation coefficients that must be computed, becausefor every XY correlation, there is a corresponding YX correlation. It isalso apparent that the compact notation of the correlation coefficienttable greatly facilitates programming the sub-routine that populates thetable. If the computations were to be done with only two correlations ineach row (XY and YX), there would be over 600 rows for 50 partcharacteristics.

The preceding method for populating the correlation coefficient table isone embodiment of the table population/compression algorithm. Aspreviously mentioned, to maintain the usual and customary conventionconsistent with industry standards, the data for a single articlecharacteristic is vertically arranged in a column. The methods describedherein would function equally well if the data for a single articlecharacteristic was horizontally arranged in a row and the algorithm wasadapted for that data structure. For that case, the average correlationcoefficient would be computed by taking the average of a row ofcorrelation coefficients rather than a column.

B.2.a.4. Alternative Embodiment

In one embodiment, a user may use the functionality discussed above tocomplete the selection of a predictor characteristic and view thescatter diagrams with the predictor characteristic as the x-axisvariable and including specification limits, and optionally predictionintervals. Based on such scatter diagrams, the user may select thepredicted characteristics that are robust (insensitive to changes inprocess settings) and eliminate such article characteristics from thedata set to eliminate “noise.” This selection can also be accomplishedanalytically based on the slope and Y-intercept of the regression model,the location and slope of the prediction intervals and the value of theupper and lower specification limits for both the x-axis and y-axisvariables. In one form, such article characteristics have predictionintervals that do not intersect the predicted article characteristicspecification limits. In this context, they constitute “noise” inselection of a predictor characteristic. The user then re-runs theselection of the best predictor based on the revised (reduced) data set.

B.2.b. Minimum And Maximum Values For Predictor Characteristic

To facilitate understanding of an embodiment of the present invention,it is useful to consider a simple situation where there are only twoarticle characteristics of interest. One of the article characteristicsis selected as the predictor characteristic using methods describedabove. The regression model establishes the relationship between thepredictor characteristic and the remaining article or predictedcharacteristic. This situation is illustrated in FIG. 12. Theintersection of the regression model with the upper and lowerspecification limits of the predicted characteristic (Y) determine thevalues of the predictor characteristic (X) above which the predictedcharacteristic does not meet specification. See Section II.B.1.b.,supra. The intersection between the regression model and the upperspecification limit for Y is defined as P-max. See FIG. 12. Theintersection between the regression model and the lower specificationlimit for Y is defined as P-min. It can readily be seen that as long asthe predictor characteristic (X) is between the values of P-min andP-max, then the predicted characteristic must be within specificationlimits. For this regression model, where there is perfect correlationbetween the predictor characteristic and the predicted characteristic,it can definitively be said that if the predictor characteristic isgreater than P-max or less than P-min, then the predicted characteristicwill be outside its specification limits. Another way of expressing thisis to define the distance between P-min and P-max as P-range. It canthen be said that as long as the predictor characteristic is withinP-range, then the predicted characteristic will be within specification.

Because the regression model seldom has a perfect degree of correlation,there is uncertainty in using one part characteristic to predict theother. The prediction intervals associated with the regression modelplace limits on the uncertainty associated with predicting one partcharacteristic given a value for the other part characteristic. FIG. 13illustrates use of the prediction intervals associated with theregression model to remove the effect of such uncertainties. As FIG. 13shows, two lines are shown located in the vicinity of and approximatelyparallel to the regression line. These lines are the upper and lowerprediction intervals that more or less bound the data points around theregression line. For didactic purposes, the upper and lower predictionintervals are shown as being straight lines; in practice, however, theyare generally curvilinear. As FIG. 13 provides, because of the scatterof the data points, the maximum allowable range for the predictorcharacteristic is more restricted. Specifically, the predictorcharacteristic can be no larger than the value associated with theintersection of the upper prediction interval and the upperspecification limit for the predicted characteristic, or the upperspecification limit for the predictor characteristic (whichever issmaller). In a similar fashion, the predictor characteristic can be nosmaller than the value associated with the intersection of the lowerprediction interval and the lower specification limit for the predictedcharacteristic, or the lower specification limit for the predictorcharacteristic (whichever is larger). In other words, P-max is the moreconstraining (smaller) of the upper specification limit for thepredictor characteristic and the intersection of the upper predictioninterval with the upper specification limit for the predictedcharacteristic. Similarly, P-min is the more constraining (larger) ofthe lower specification limit for the predictor characteristic or theintersection of the lower prediction interval with the lowerspecification limit for the predicted characteristic. As long as thepredictor characteristic is between P-min and P-max, the predictedcharacteristic will be within its specification limits. In oneembodiment, it can be a matter of judgment as to how “wide” theprediction intervals should be. In one embodiment, process analysisapplication 100 uses typical “width” parameters as a default setting.However, the user will have the option of overriding these defaultsettings.

B.2.c. Constraint Table For Predictor Characteristic

As discussed above, the foregoing discussion in section II.B.2.b.,supra, involved a simplified situation involving only two articlecharacteristics. In actual practice, a given part often has a largenumber of article characteristics of interest. In one embodiment,process analysis system 100 is further operative to create a constrainttable. A constraint table contains, for each predicted articlecharacteristic, the minimum (P-min) and maximum (P-max) values for thepredictor characteristic as determined above (see Sections II.B.1.b. &c., II.B.2.b., supra).

From the constraint table (see FIG. 22), the most constraining minimum(P-min*) and maximum (P-max*) values can be determined for the predictorcharacteristic. See FIG. 14. That is, the most constraining minimumvalue (P-min*) is the largest minimum value (P-min) in the constrainttable, while the most constraining maximum value (P-max*) is thesmallest maximum value (P-max) in the constraint table. FIG. 22illustrates a constraint table according to an embodiment of the presentinvention, where “none” means that the upper or lower specificationlimit for the predictor characteristic is the most constraining value asto the corresponding article characteristic. Accordingly, with anidentification of these most constraining minimum (P-min*) and maximumvalues (P-max*), a manufacturer can be confident that as long as thepredictor characteristic lies between them, the remaining predictedcharacteristics will be within specification limits.

B.2.d. Determining Producibility Targets And Ranges

Further parameters can be derived from P-min* and P-max* that will beuseful for facilitating design of articles and process inputs and forsetting process control variables. The maximum allowable range(P-range*) can be computed by subtracting the most constraining minimumvalue (P-min*) from the most constraining maximum value (P-max*). FIG.14 graphically illustrates the determination of the most constrainingminimum and maximum values and range for the simplified case of twopredicted characteristics.

In addition, a predictor production target (P-target*) can bedetermined. P-target* is the point that one would pick as the target forthe average output of the process. It is, in one embodiment, the best“producibility” point that will maximize the chances of producing partsthat are in compliance with specification limits. When properlyselected, P-target* will minimize the percentage of data points that areoutside of P-range* during production.

To avoid confusion between design target and a target for the predictordimension (P-target*), a difference in terminology should be noted. Thepredictor characteristic (P) will almost always have an engineeringdesign target (P-target). The engineering design target (or nominalvalue) is a value called for by a design engineer (e.g., a number on adrawing or in a specification). In contrast, P-target* is a targetoperating point for the process that will optimize production output, asdiscussed above.

In one embodiment the predictor production target (P-target*) isselected as the midpoint of P-range*. See FIG. 14. This would beappropriate for situations where the output of the production processwas symmetrical about its mean. Typical processes have a symmetricaldistribution that is approximately normal. If the distribution of thearticle characteristic is non-symmetrical, the target predictorcharacteristic value can be set to the average article characteristicvalue.

C. Using the Maximum Allowable Range (P-range*) And the PredictorProduction Target (P-target*)

For didactic purposes, it will be useful to clarify and define certainparameters as follows:

-   -   1. P-range* is the maximum allowable range for the predictor        characteristic. It is the range within which the predictor        characteristic must be to ensure that the remaining part        characteristics will remain in compliance with specification        limits;    -   2. P-target* is the value of the predictor characteristic        production target value. P-target* can be set at several values        within P-range*. P-target* will usually be set at the midpoint        of P-range*;    -   3. VAR is the range of variability of the predictor        characteristic associated with actual process output under        production conditions. It is determined by assessing production        output;    -   4. X-BAR is the average value of the predictor characteristic        under production. conditions. It is determined by assessing        production output;    -   5. P-target is the engineering design target value for the        predictor characteristic. It is determined by the design        engineer to optimize form, fit and function; and,    -   6. USL and LSL are the upper and lower specification limits for        the predictor characteristic based on the engineering design        tolerances. They are determined by the design engineer and        typically consider a number of factors including history        tolerances used by the organization, criticality of the part,        capability of the manufacturing organization, and other factors.

Knowing the maximum allowable range (P-range*) for the predictorcharacteristic is extremely useful. The actual process output willexhibit a certain amount of variability (VAR) and there will be a valuethat represents the predictor characteristic average process output(X-BAR). Having this information facilitates changing at least oneprocess control setting in a manner that maximizes the likelihood thatthe process generates parts that are in compliance with thespecification.

If it has been decided that P-range* is too “constrained”, then a secondpart characteristic can be measured to “open up” the “constraints” onP-range*.

There is also great utility in comparing the size of the actual processvariability (VAR) for the predictor characteristic to the maximumallowable range (P-range*). The following situations are possible:

-   -   1. If the actual process variability (VAR) is greater the        maximum allowable range (P-range*), then a portion of the parts        produced by the process will always be out of compliance.    -   2. If the actual process variability (VAR) is equal to the        maximum allowable range (P-range*) and the average process        output (X-BAR) is centered within the maximum allowable range,        then nearly all parts produced by the process will be in        compliance, but there will be no room for error or for shifts in        the process output.    -   3. If the actual process variability (VAR) is smaller than and        lies within the maximum allowable range (P-range*), then nearly        all parts will be in compliance and there will be a greater        margin of safety against errors or shifts in process output.

The present invention creates, for situation number 3 an excellentopportunity for the process engineer to investigate shifting the averageprocess output (X-BAR) closer to the engineering design target(P-target) for the predictor part characteristic.

There is also great utility in comparing the average process output(X-BAR) for the predictor characteristic to its production target(P-target*). The following situations are possible, assuming that theprocess output distribution is symmetrical and the predictorcharacteristic target value (P-target*) is set at the midpoint of itsmaximum allowable range:

-   -   1. The closer the average process output (X-BAR) for the        predictor characteristic is to the predictor production target        (P-target*), the greater the likelihood that the process will        produce parts that are in compliance.    -   2. When the average process output (X-BAR) is equal to the        predictor production target (P-target*), the chances are        maximized that the process will produce parts that are in        compliance.

Similar conclusions can be reached even if the process outputdistribution is not symmetrical. In this situation, P-target* should beset at the point where the tails of the distribution have equal areasoutside of P-range*.

Thus, the present invention facilitates determining the differencebetween the average process output (X-BAR) and the predictor productiontarget (P-target*). This difference establishes both the magnitude andthe direction that the average process output (X-BAR) should be shifted.With this knowledge, it is then possible to adjust one or more processcontrol settings to move the average process output along the regressionline to or closer to the predictor production target (P-target*).

The present invention provides further utility. It now becomes possibleto determine whether the actual process variability (VAR) is too largerelative to the maximum allowable range (P-range*). If this is the case,then a first option is to reduce the process variation. A second optionis to increase the magnitude of the design tolerances. A third option isto do some combination of the previous two alternatives. The presentinvention can greatly facilitate efficiency and cost savings byrequiring that the various process capability analyses discussed in thissection be performed only one time for only the predictor characteristicinstead of the 30 or 40, or however many total part characteristics,involved.

Moreover, the constraint table values provide other useful informationfor the design engineer. For example, if the decision is made toincrease the size of the design tolerances, the constraint tablefacilitates a prioritized determination to be made as to 1.) whichspecification limit (upper or lower) for 2.) which articlecharacteristic is most constraining and should be the first to berelaxed. This step can be repeated as often as desired, working“outward” from the most constraining to the least constraining partcharacteristic. The design engineer can also assess the impact ofrelaxing each tolerance on product performance and factor thisinformation into the decision-making process.

The present invention creates still more utility. The design engineernow has information that enables a study to be conducted that evaluatesthe tradeoff between product performance and producibility. In addition,the design engineer may also, if circumstances permit, change the designtarget to the determined predictor characteristic target (P-target*),and make changes elsewhere in the system to compensate (if compensationis even required, for the change in the design target).

To condense the next series of comments, P-range*, VAR, and TOL (thedifference between the upper and lower specification limits), will berepresented by A, B, and C. In a similar fashion, P-target*, X-BAR andP-target will be represented by X, Y, and Z. The present inventionfacilitates the following comparisons:

-   -   1. A versus B;    -   2. A versus C;    -   3. B versus C;    -   4. X versus Y;    -   5. X versus Z; and,    -   6. Y versus Z.        As previously noted, there is exceptionally valuable information        that can be gained from these comparisons.        D. Application Overview And Summary

FIG. 21 summarizes the concepts discussed above and illustrates a methodaccording to an embodiment of the present invention. For didacticpurposes, an injection molding process is described. As FIG. 21 shows,the design of a part, for example, yields various design targets andspecification limits for the article characteristics (602), which yieldsthe design and fabrication of a mold including at least one cavitydefining a part (604). Other inputs to the process include noisevariables (606) and process control settings (607). The process (208)yields either experimental output (610) or production output (630), asdiscussed below.

As FIG. 21 shows, embodiments of the present invention can be used tofacilitate the design and engineering processes associated withdesigning a part and/or engineering a process that will ultimately yieldacceptable parts for production output. As discussed above, in oneembodiment, a process operator generates a set of parts having a rangeof variation as to a plurality of article characteristics (experimentaloutput 610). The article characteristics associated with theexperimental output 610, or a sample thereof, are assessed and analyzedusing the correlation and regression analysis methods discussed above(612). With the information gleaned from these analysis methods, theuser may decide to change tolerance limits (618) and/or design targets(620). In addition, the user may decide to change process inputs (616)and/or adjust control variables (614).

As discussed above, in one embodiment, variation is induced in the partcharacteristics during an experimental production run. One of theby-products of that experimental production run is that the user learnswhich process settings have a major impact on the part characteristics.That knowledge enables the user to adjust a small number of processsettings to position product output at any predetermined point along theregression model. In the case of injection molding, for example, theuser may find that changing just one pressure setting, or onetemperature setting, or one speed setting will be sufficient totranslate the joint output of the article characteristics along theregression line.

Furthermore, the user may select a predictor characteristic (636) basedon the correlation and regression analysis (612) to facilitatemeasurement of production output (630). For example, by analyzing theremaining article characteristics in relation to the predictorcharacteristic the user may identify robust predicted characteristics(i.e., article characteristics that will always be within tolerancelimits) and eliminate them from measurement (step 634). Alternatively,or in conjunction therewith, the user may determine the maximumallowable range for the predictor characteristic and determine whetherproduction output 630 complies with specification limits by measuring asingle article characteristic (the predictor characteristic) (632).

As discussed above, to shift output along the regression line, aninjection mold operator can change one or more process controlvariables, such as pressure, temperature, speed, etc. For illustrativepurposes, an example of changing process inputs in the injection moldingindustry would be to change an internal dimension of a mold cavity. Theregression line can be shifted vertically. In FIG. 6, this would beaccomplished by changing the size of the remaining articlecharacteristic. A reduction in size would be accomplished by addingmaterial to the inside of the mold at the location of that articlecharacteristic. This would decrease the size of the produced article andwould shift the regression line vertically downwards. The size of therequired shift would be computed by determining the distance theregression line was offset from the target intersection. Thus, thelocation of the regression line relative to the target intersectionprovides information that can be used to determine which direction theregression line needs to be shifted and the magnitude of that shift.

An alternate method of shifting the regression line in FIG. 6 would beto shift it horizontally. In order for the regression line to passthrough the target intersection in FIG. 6, it must be shifted to theright. This shift would be accomplished by changing the mold dimensionfor the predictor characteristic. A shift to the right means that thesize of the predictor characteristic is increased. An increase in sizerequires an increase in the size of the mold cavity for the predictorcharacteristic (here, a dimension). This can be accomplished by removingmaterial from the interior of the mold. The size of the required shiftis determined by computing the horizontal distance between theregression line and the target intersection.

Yet another method of shifting the regression line is to create theshift by changing some combination of mold dimensions for both thepredictor and at least one of the remaining article characteristics. Inthe specific example shown in FIG. 6, the regression line is shifted ina direction perpendicular to itself. This, in effect, is the shortestpossible shift that can be done to position the regression line throughthe target intersection. In this case, the shift would be accomplishedby a decrease in the size of the predicted characteristic and anincrease in the size of the predictor characteristic. In the case of aplated part, an example of the two article characteristics could be thepost-plating length and width dimensions of the part. In this case, thepre-plating length and width dimensions of the part would be consideredas the process inputs.

FIG. 7 illustrates a method used to produce articles that havecharacteristics that are superimposed on or congruent with the targetintersection. The embodiment illustrated in FIG. 7 consists of atwo-step process. In step one, the regression line is shifted so that itintersects the target intersection. In this example, the regression lineis shifted down and to the right. As indicated before, the regressionline can be shifted horizontally, vertically, or both. In step two, thecharacteristic position is shifted, for this particular example, alongthe regression line in the direction of smaller dimensions until theposition is congruent with the target intersection. In practice, ofcourse, the direction that the characteristic position will need to beshifted will depend on the location of the initial characteristicrelative to the target intersection and the slope of the regressionline.

For didactic purposes, the language contained in this application refersto the use of techniques to “locate” or “position” or “determine theintersection” or “determine the range” or other terminology that mightbe used from a graphical perspective. Virtually all analyticaltechniques described herein document can be accomplished eithergraphically or analytically. It is to be appreciated that analyticaltechniques can be used when graphical techniques are described andgraphical techniques can be used when analytical techniques aredescribed. Indeed, a preferred embodiment of the present inventionperforms all computations, calculations, locations and determinationsusing analytical techniques. Graphical displays are created for theconvenience and understanding of the user.

Also for didactic purposes, the language in this application refers to aregression line. It is noted above that the regression “line” need notbe a straight line but may be curvilinear. It should also be noted thatthe regression model is frequently shown, for didactic purposes, asbeing a single line. It should be noted the regression model in apreferred embodiment of this invention includes use of predictionintervals.

It is to be noted, that for didactic purposes, the effect of changes inprocess control settings has been illustrated in terms of their effecton a single remaining (predicted) article characteristic. It is to beunderstood that the effect of changes in process control settings can bedetermined for more than two article characteristics. Similarly, it isto be understood that the effect of changes in process inputs has beenillustrated in terms of their effect on a single regression model. It isto be understood that the effect of changes in process control settingscan be determined for than one regression model.

For didactic purposes, it has been assumed that the objective ofintroducing changes in either the process control settings and/orprocess inputs has been to move the joint operating position(s) and/orregression model(s) closer to one or more target intersections. It is tobe further understood that these changes can be made to move the jointoperating position(s) and/or regression model(s) to any desiredposition.

Finally, for didactic purposes, as noted immediately above, it has beenassumed that changes have been introduced to optimize articlecharacteristics relative to one or more criteria. It is to be understoodthat the algorithms, models and concepts set forth herein can be used toachieve the opposite effect. For example, it is possible to determinethe required change in a joint operating point and/or regression modelposition needed in order to achieve a desired change in a processsetting and/or process input. One objective of doing this could be tomove a process control setting away from a dangerous or harmful setting.Another objective of doing this could be to match the production processand/or engineering design parameters to specific process inputs such aspre-configured raw material shapes.

Lastly, although the present invention has been described as operatingin connection with injection molding processes, the present invention,as discussed above, has application to a variety of processes. Forexample, the present invention has application to plating andsemiconductor manufacturing, as well as any other process where amaterial is added, removed, or changed in form or structure.Accordingly, the present invention has been described with reference tospecific embodiments. Other embodiments of the present invention will beapparent to one of ordinary skill in the art. It is, therefore, intendedthat the claims set forth below not be limited to the embodimentsdescribed above.

1. An apparatus facilitating design, manufacturing, and other processes comprising: a processor; a memory; a software application, physically stored in the memory, comprising instructions operable to cause the processor and the apparatus to: receive a plurality of article characteristic values associated with a set of articles having a range of variation as to a plurality of article characteristics resulting from a process; select a predictor characteristic from the plurality of article characteristics; and, determine the regression model(s) between the predictor characteristic and at least one of the remaining article characteristics in the plurality of article characteristics.
 2. The apparatus of claim 1 wherein the software application further comprises instructions operable to cause the processor and the apparatus to receive a target value for the predictor characteristic and a target value for at least one remaining article characteristic; and determine the intersection of the target value for the predictor characteristic and the target value of a first remaining article characteristic relative to the regression model between the predictor characteristic and the first remaining article characteristic.
 3. The apparatus of claim 1 wherein the software application further comprises instructions operable to cause the processor and the apparatus to determine the respective upper and lower prediction intervals associated with the regression model(s) between the predictor characteristic and at least one of the remaining article characteristics.
 4. The apparatus of claim 3 wherein the software application further comprises instructions operable to cause the processor and the apparatus to receive lower and upper specification limits for said at least one of the remaining article characteristics; locate the lower and upper specification limits associated with said at least one of the remaining article characteristics; receive lower and upper specification limits for the predictor characteristic; locate the upper specification limit associated with the predictor characteristic; and determine a maximum article characteristic value for the predictor characteristic by selecting the lesser of (1) the upper specification limit for the predictor characteristic and (2) the value of the predictor characteristic at which the upper prediction interval intersects the upper specification limit for said at least one remaining article characteristic.
 5. The apparatus of claim 4 wherein the software application further comprises instructions operable to cause the processor and the apparatus to repeat determining a maximum article characteristic value for a desired number of remaining article characteristics in the plurality of article characteristics; and determine the most constraining maximum article characteristic value for the predictor characteristic by selecting the lowest maximum article characteristic value.
 6. The apparatus of claim 1 wherein the software application further comprises instructions operable to cause the processor and the apparatus to select the predictor characteristic based at least in part on an assessment of the capability of each article characteristic to be predictive of all or a subset of the article characteristics in the plurality of article characteristics.
 7. The apparatus of claim 6 wherein the software application further comprises instructions operable to cause the processor and the apparatus to calculate the correlation coefficients between all or a subset of the article characteristics; determine, based on the calculated correlation coefficients, a value indicating the predictive capability of a first article characteristic relative to all other article characteristics; repeat determining a value indicating the predictive capability for said all or a subset of the article characteristics; and select a predictor characteristic based at least in part on the values indicating the predictive capabilities of the article characteristics.
 8. A computer program product, physically stored on a machine-readable medium, for facilitating design, manufacturing, and other processes, comprising instructions operable to cause a programmable processor to: receive a plurality of article characteristic values associated with a set of articles having a range of variation as to a plurality of article characteristics resulting from a process; select a predictor characteristic from the plurality of article characteristics; and, determine the regression model(s) between the predictor characteristic and at least one of the remaining article characteristics in the plurality of article characteristics.
 9. The computer program product of claim 8 wherein the instructions are further operable to cause the programmable processor to: select the predictor characteristic based at least in part on an assessment of the capability of each article characteristic to be predictive of all or a subset of the article characteristics in the plurality of article characteristics.
 10. The computer program product of claim 9 wherein the instructions are further operable to cause the programmable processor to: calculate the correlation coefficients between all or a subset of the article characteristics; determine, based on the calculated correlation coefficients, a value indicating the predictive capability of a first article characteristic relative to all other article characteristics; repeat determining a value indicating the predictive capability for said all or a subset of the article characteristics; and select a predictor characteristic based at least in part on the values indicating the predictive capabilities of the article characteristics.
 11. The computer program product of claim 10 wherein the predictor characteristic is selected as the article characteristic associated with the value indicating the highest predictive capability.
 12. A computer-implemented method facilitating design and manufacturing processes, the method comprising receiving a plurality of article characteristic values associated with a set of articles having a range of variation as to a plurality of article characteristics resulting from a process; and computing, for each article characteristic, a predictor value indicative of the capability of an article characteristic to be predictive of all or a subset of the article characteristics in the plurality of article characteristics.
 13. The computer-implemented method of claim 12 wherein the computing step comprises calculating the correlation coefficients between all or a subset of the article characteristics; determining, based on the calculated correlation coefficients, a value indicating the predictive capability of a first article characteristic relative to all other article characteristics; and repeating the determining step for said all or a subset of the article characteristics.
 14. The computer-implemented method of claim 12 further comprising ranking the article characteristics based on the corresponding predictor values.
 15. The computer-implemented method of claim 12 further comprising displaying the predictor values to a user.
 16. The computer-implemented method of claim 12 further comprising receiving a selection of a predictor characteristic from the plurality of article characteristics; and, determining the regression model(s) between the predictor characteristic and at least one of the remaining article characteristics in the plurality of article characteristics.
 17. The computer-implemented method of claim 16 further comprising graphically displaying the regression model(s).
 18. The computer-implemented method of claim 16 further comprising receiving a target value for the predictor characteristic and a target value for at least one remaining article characteristic; and determining the intersection of the target value for the predictor characteristic and the target value of a first remaining article characteristic relative to the regression model between the predictor characteristic and the first remaining article characteristic.
 19. The computer-implemented method of claim 16 further comprising determining the respective upper and lower prediction intervals associated with the regression model(s) between the predictor characteristic and at least one of the remaining article characteristics.
 20. The computer-implemented method of claim 16 further comprising determining the respective upper and lower prediction intervals associated with the regression model(s) between the predictor characteristic and at least one of the remaining article characteristics. 